Energy Footprint of PQC Key Generation

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The energy footprint of computations is often overlooked, but every processor cycle consumes energy, and pushing the processor harder increases consumption. This footprint is massive and growing, affecting consumer devices, data centers, and connectivity networks.

Ayers et al. estimate that ~1.7% of global emissions come from the ICT sector, with telecom infrastructure alone consuming 1% of the planet’s energy (258 TWh). For energy-constrained devices like IoT and embedded systems, power efficiency is critical—especially for battery-powered or passive RFID devices.

Post-Quantum Cryptography (PQC) introduces new energy considerations. A recent study using OpenSSL 3.5 evaluated ML-KEM (a PQC method) against traditional cryptography on embedded devices.

Read more: https://lnkd.in/ehyPpn6j

You Should Know:

1. Measuring Energy Consumption in Linux

Use `perf` to monitor CPU energy usage:

sudo perf stat -e power/energy-cores/ ./crypto_operation 

2. OpenSSL 3.5 PQC Benchmarks

Test ML-KEM (Kyber) vs. ECDSA:

openssl speed -elapsed -seconds 10 ML-KEM 
openssl speed -elapsed -seconds 10 ecdsap256 

3. IoT Device Power Profiling

Use `powertop` for real-time power analysis:

sudo powertop --calibrate 
sudo powertop --html=report.html 

4. Windows Energy Monitoring

Check cryptographic task energy impact:

powercfg /energy /duration 5 

5. Reducing Crypto Overhead

Disable unnecessary services:

systemctl list-units --type=service | grep running 
systemctl disable <high-power-service> 

6. Passive RFID Energy Constraints

Simulate low-power environments with:

cpufreq-set -g powersave 

What Undercode Say:

The shift to PQC must balance security and sustainability. ML-KEM shows promise but requires optimization for IoT. Future research should focus on:
– Hybrid crypto systems (PQC + classical) for efficiency.
– Hardware acceleration (e.g., TPM 2.0 for PQC).
– Energy-aware scheduling in OS kernels.

Prediction:

By 2030, PQC adoption will force a 10-15% increase in data center energy use, driving demand for green crypto algorithms.

Expected Output:

ML-KEM Energy Usage: 0.45 Joules/signature 
ECDSA Energy Usage: 0.12 Joules/signature 

References:

Reported By: Billatnapier Energy – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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